This article introduces tools and methods for monitoring and tuning Go language performance, including how to use and precautions for tools such as pprof, expvar and trace, as well as some common methods for performance tuning, such as reducing memory allocation, avoiding frequent garbage collection, and avoiding excessive database query. For different programs, different optimization methods should be adopted according to actual conditions, and program performance should be continuously optimized in continuous practice.
1. What is performance monitoring and optimization
Performance monitoring mainly refers to real-time monitoring and collection of the system's operation, including indicators such as CPU occupancy, memory occupancy, and network traffic; performance tuning refers to analyzing based on the system's monitoring data, and then optimizing and adjusting the system to achieve higher operating efficiency and faster response speed.
2. Performance monitoring and tuning tools for Go language
In Go language, a series of performance monitoring tools are provided, such as pprof, expvar and trace. The following will introduce the usage methods and precautions of these tools.
2.1 pprof
pprof is a performance analysis tool for Go language. It can analyze the CPU and memory usage of the program and generate corresponding analysis reports. We can present the performance analysis data interactively to better understand the performance bottlenecks of the program.
We can passgo tool pprofTo perform performance analysis, you need to add the following code to the program:
import _ "net/http/pprof"
Then start the service and usego tool pprofCommand connects to the specified process:
go tool pprof http://localhost:6060/debug/pprof/profile
After the connection is successful, we can start analyzing the program performance. pprof will display the program's flame diagram. We can locate the program's performance bottleneck through the flame diagram and then perform corresponding optimizations.
2.2 expvar
expvar can be used to expose the internal state and metrics of a program. The Go language standard library already comes with some built-in metrics, such as MemStats, HeapAlloc, etc., which we can access through the HTTP interface.
We need to add the following code to the program:
import (
"expvar"
"net/http"
)
func main() {
("/debug/vars", func(w , r *) {
(func(kv ) {
(w, "%s: %s\n", , ())
})
})
(":8080", nil)
}Then we can passcurl http://localhost:8080/debug/varsCommands to access the internal status and metrics of the program.
2.3 trace
trace can be used to analyze program execution and performance bottlenecks. We can usego tool traceCommand to view the analysis report generated by trace.
We need to add the following code to the program:
import (
"log"
"net/http"
_ "net/http/pprof"
"os"
"runtime/trace"
)
func main() {
f, err := ("")
if err != nil {
("failed to create trace file: %v", err)
}
defer ()
err = (f)
if err != nil {
("failed to start trace: %v", err)
}
defer ()
("/", func(w , r *) {
([]byte("hello, world"))
})
(":8080", nil)
}Then we can passgo tool trace Commands to view the generated analysis report and then analyze the performance bottlenecks of the program.
3. Methods of performance tuning
After understanding the performance monitoring tools of Go, you can perform performance tuning next. Below are some commonly used performance tuning methods.
3.1 Reduce memory allocation
Memory allocation is a key operation that takes up program running time. To reduce memory allocation, we can use . to reuse objects in the object pool.
type Object struct {
// ...
}
var objectPool = {
New: func() interface{} {
return &Object{}
},
}
func GetObject() *Object {
return ().(*Object)
}
func PutObject(obj *Object) {
(obj)
}In the above code, we created an object poolobjectPool, used to cache objectsObject. When you need to use an object, first get the object from the object pool. If the object pool is empty, it will be automatically called.Method to create a new object and then return the object; after use is completed, put the object back into the object pool for next use.
3.2 Avoid frequent garbage collection
Frequent garbage collection will take up the program's running time. To avoid frequent garbage collection, we can use . to implement a wait pool.
type WaitPool struct {
lock
cond *
maxLen int
curLen int
}
func NewWaitPool(maxLen int) *WaitPool {
pool := new(WaitPool)
= maxLen
= (&)
return pool
}
func (pool *WaitPool) Wait() {
()
defer ()
for >= {
()
}
++
}
func (pool *WaitPool) Done() {
()
defer ()
--
()
}In the above code, we implement a waiting poolWaitPool, used to limit the number of waitingmaxLen. When you need to wait, call()Method to add a waiting pool. If the current waiting number has reached the upper limit, it will automatically block; call it after the task is completed()Method, reduce the current waiting number by 1 and wake up the next task.
3.3 Avoid over-querying the database
Database queries are also a key operation that takes up program running time. To avoid excessive database queries, we can use techniques such as cache, timer, and read-write separation to optimize the program.
When using cache, pay attention to the cached update strategy to avoid inconsistent with the cached data. When using a timer, pay attention to the time interval of the timer task to avoid the task occupies too much CPU resources. When using read and write separation, pay attention to the consistency of read and write operations.
4. Summary
This is the article about the tools and methods of Go language performance monitoring and tuning. For more relevant Go language performance monitoring and tuning content, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!